System and method for labeling a therapeutic value to digital content based on meta-tags

ABSTRACT

Disclosed is a system and method for labeling digital content based on intended psycho-emotional effect on a viewer in order to provide an advisory prior to viewing comprising the steps of: uploading or stream-initiating a digital content by a viewer; selecting at least one intended psycho-emotional effect from the content based on scraped meta-data tags of the content; and overlaying a digital nutrition label to the digital content corresponding to the scraped psycho-emotional effect from the content, wherein the overlaid label displays at least one of an effect or triggered neurotransmitter associated with the effect in order to provide an advisory to the viewer prior to viewing the labeled digital content.

TECHNICAL FIELD

This invention relates generally to the field of electroniccommunications and the transmittance of such communications. Morespecifically, the invention discloses a new and useful method forself-rating and autonomously rating a therapeutic value to digitalcontent.

BACKGROUND

In the past few decades, the availability and use of electroniccomputing devices, such as desktop computers, laptop computers, handheldcomputer systems, tablet computer systems, and cellular phones havegrown tremendously, which provide users with a variety of new andinteractive applications, business utilities, communication abilities,and entertainment possibilities.

One such communication ability is electronic messaging, such astext-based, user-to-user messages. Electronic messaging has grown toinclude a number of different forms, including, but not limited to,short message service (SMS), multimedia messaging service (MMS),electronic mail (e-mail), social media posts and direct messages, andenterprise software messages. Electronic messaging has proliferated tosuch a degree that it has become the primary mode of communication formany people.

While electronic messaging can be a particularly efficient mode ofcommunication for a variety of reasons—instant delivery, limitlessdistance connectivity, recorded history of the communication—electronicmessaging does not benefit from the advantages of in-personcommunication and telecommunication. For example, when communicating viatelecommunication, a person can adjust, alter, or augment the content oftheir message to an intended recipient through tone, volume, intonation,and cadence. When communicating in-person, or face-to-face, a person canfurther enhance or enrich their spoken words with eye contact and shiftof focus, facial expressions, hand gestures, body language, and thelike. In electronic messaging, users lack these critically importantsignals, clues, and cues, making it difficult for people to convey thesubtler aspects of communication and deeper intent. As a result, issuesof meaning, substance, and sentiment are often lost or confused inelectronic messages, which can, and very often does, result in harmfulor damaging misunderstandings. Miscommunications can be particularlydamaging in interpersonal and business relationships.

Another unintended effect of our overreliance on electroniccommunication is the impairment of emotional and mental health. In arecent article published in the American Journal of Psychiatry, Dr.Jerald Block wrote “technology addiction is now so common that it meritsinclusion in the Diagnostic and Statistical Manual of Mental Disorders,the profession's primary resource to categorize and diagnose mentalillnesses.” He went on to further state that the disorder leads to angerand depression when the tech isn't available, as well as lying, socialisolation and fatigue. Our devices and experiences from said devices(receiving likes, comments and shares on social media) are in essence adrug dealer and drugs, respectively: Having the capability of doling outthe same kind of dopamine hit as a tiny bump of cocaine. In effect,creating the typical addiction/dependency vicious cycle and all of theattendant consequences.

According to psychotherapist, Nancy Colier, author of “The Power ofLife”, “We are spending far too much of our time doing things that don'treally matter to us . . . [and become] disconnected from what reallymatters, from what makes us feel nourished and grounded as humanbeings.” Based on her findings, the average person checks theirsmartphones 150 times per day, or every six minutes. Furthermore, theaverage young adult sends on average 110 texts per day and 46% ofrespondents checked that their devices are something that they couldn'tlive without.

With this kind of digital ubiquity, it is becoming readily apparent thatany solution to the problem involving curtailing or augmenting userbehavior is not a realistic approach. Current approaches espoused byexperts involve any one of, or combination of, the following:Downloading an app (Moment, Alter, etc.) that locks or limits phoneusage upon reaching a pre-specified limit; disabling notifications fromyour phone settings; keeping the blue-hued light of your smartphone awayfrom your place of rest; and even buying and carrying around a dummyphone.

There is a void for a solution that takes into account ubiquitous usageand provides delivery of pro-mental and emotional healthcontent—personalized to the user, much like the way therapeutics havebecome narrowly tailored—to counter all of the digital-mediated illeffects plaguing our society. These effects will only logarithmicallygrow as we transition into the IoT era—where we will be exposed tothousands of internet-enabled objects (each capable of deliveringcontextualized analytics and provisioning) as part of our day-to-dayliving.

What's more, there is a void for a solution that allows for aself-generated or system-generated rating for therapeutic value ofdigital content. In other words, currently, there is no technologicalsolution for a standardized rating of digital content based on itspsycho-emotional effects on the targeted user or a general user derivedfrom meta-tags or back-end data. Furthermore, there is currently nosolution with downstream provisioning of digital/interactive contentbased on the rated content.

SUMMARY

Disclosed is a method and system for imposing a dynamic sentiment vectorto an electronic message. In one embodiment of the invention, the methodcomprises: receiving a text input comprising message content from anelectronic computing device associated with a user; parsing the messagecontent comprised in the text input for emotionally-charged language;assigning a sentiment value, based on the emotionally-charged language,from a dynamic sentiment value spectrum to the text input; and, based onthe sentiment value, imposing a sentiment vector, corresponding to theassigned sentiment value, to the text input, the imposed sentimentvector rendering a sensory effect on the message content designed toconvey a corresponding sentiment.

In another embodiment of the invention, the method comprises: receivinga text input comprising message content from an electronic computingdevice associated with a user; converting the message content comprisedin the text input received from the electronic computing device intoconverted text in a standardized lexicon; parsing the converted text foremotionally-charged language; generating a sentiment value for the textinput from a dynamic sentiment value spectrum by referencing theemotionally-charged language with a dynamic library ofemotionally-charged language; and, based on the sentiment value,imposing a sentiment vector to the text input, the imposed sentimentvector rendering a sensory effect on the message content designed toconvey a corresponding sentiment.

For example, in one application of the invention, a user can write andsubmit a text message on the user's cellular phone for delivery to theuser's best friend. After receiving the text message, the invention cananalyze the message content of the text message and determine, based onthe verbiage, syntax, and punctuation within the message content, thatthe user is attempting to convey excitement through the text message.The invention can then apply a visual filter of red exclamation pointsor other illustrative, performative, or kinetic attributes to the textmessage, indicating the excitement of the user, before the text messageis delivered to the user's best friend.

In another example of one application of the invention, a user can writeand submit a direct message through a social media application (e.g.,Instagram, Facebook, SnapChat) on the user's mobile phone for deliveryto a second user. After receiving the direct message, the invention canuse a camera built into the user's mobile phone to capture an image ofthe user's face and analyze aspects of the user's face (e.g., curvatureof the lips, motion of the eyes, etc.) to determine the user's mood orexpression. Based on the user's mood or expression, the invention canthen apply a vibration pattern to the direct message before the directmessage is delivered to the second user.

In another object of the invention, sentiment and cues of the usersemotional or mental state is not gleamed by referencing a parsed userinput against a dynamic library of emotionally-charged language togenerate a sentiment value and vector for overlaying the said input.Rather, the emotional and mental state (EMS) of the user is chosen bythe user or determined by the system based on user engagement with theinterface or content. Once the EMS of the user is defined, carefullycurated and efficacious content is delivered to the user to combat thedefined EMS.

In one aspect, a method is provided for delivering a digitaltherapeutic, specific to a user-chosen emotional or mental state (EMS),the method comprising the steps of: recognizing at least one EMSselected by the user from a plurality of EMS, the selected EMSindicating at least one of a feeling, sensation, type of discomfort,mood, mental state, emotional condition, or physical status of the user.Once the EMS is defined, the method then calls for pushing aprimary-level message personalized to the user based on at least onestored message coupled to the selected EMS. Finally, pushing at least asecondary-level message personalized to the user based on athreshold-grade match of the user response to the pushed primary-levelmessage with at least one stored response coupled to a storedprimary-level message, whereby the user and stored response is a measureof at least one of a reaction, compliance, engagement, or interactivitywith the pushed and, or stored primary-level message. The primary andsecondary-level messages may contain at least one of a text, image,sound, video, art asset, suggested action or recommended behavior. Theefficaciousness or therapeutic value of the primary or secondarymessages are validated by at least one—and typically two—independentsources of clinical research or peer-reviewed science, as verified by acredentialed EMS expert.

In another aspect, once the EMS is defined, the method may call forpushing at least a single-level message. The at least single message maycontain at least one of a text, image, sound, video, art asset,suggested action or recommended behavior. Again, the efficaciousness ortherapeutic value of the primary or secondary messages are validated byat least one- and typically two-independent sources of clinical researchor peer-reviewed science, as verified by a credentialed EMS expert.

In yet another aspect, a system is described and claimed for deliveringthe digital content of validated therapeutic efficacy. The system maycomprise an EMS store; at least a primary message prescriber; aprocessor coupled to a memory element with instructions, the processorwhen executing said memory-stored instructions, configure the system tocause: at least one EMS from a plurality of EMS in the EMS store to beselected by the user, said selected EMS indicating at least one of afeeling, sensation, type of discomfort, mood, mental state, emotionalcondition, or physical status of the user; and the at least primarymessage prescriber pushing a primary-level message personalized to theuser based on at least one stored message coupled to the selected EMS.

In yet other aspects, at least a secondary message prescriber isincluded, wherein the at least secondary message prescriber pushes atleast a secondary-level message personalized to the user based on athreshold-grade match of the user response to the pushed primary-levelmessage with at least one stored response coupled to a storedprimary-level message, whereby the user and stored response is a measureof at least one of a reaction, compliance, engagement, or interactivitywith the pushed and, or stored primary-level message.

In both aspects (primary or at least secondary message prescribers), themessages or content may contain at least one of a text, image, sound,video, art asset, suggested action or recommended behavior. Much like inthe method aspects, the therapeutic value of the messages or content arevalidated by at least one—and typically two—independent sources ofclinical research or peer reviewed published science and selected by acredentialed EMS expert.

Whether the sentiment or cues are generated by the system or defined bythe user, content is being overlaid or delivered to enhance intonation,heighten digital communication, obviate ambiguity, boost mood, supportself-esteem, inspire wellness, and aid in the longitudinal andnon-interventional care for people in distress or need—leveraging afamiliar and known modality (digital devices). According to the claimedinvention, a whole ecosystem of receiving and delivering modalities areprovided for a host of digital therapeutics. The digital therapeuticofferings—with the aid of Artificial Intelligence (AI), machinelearning, and, or predictive EMS assessment tools—may deliverincreasingly personalized solutions uniquely tailored to aid eachsubscriber. Such non-interventional, anonymous, and device-centricsolutions are far more appropriate to combat the rising ill-effects ofdevice dependency—rather than pharmaceutical dosing, in-patienttreatment, and altering device behavior.

In another aspect of the invention, the user or system may generate arating for a therapeutic value of digital content. The claimed inventionclaims and discloses a technological solution for a standardized ratingof digital content based on its psycho-emotional effects on the targeteduser or a general user. The user may then engage with the contentaccordingly. Forms of engagement may be suggested, prompted, or pushedbased on the uploaded and rated content. It is one object to enable asystem and method for labeling a therapeutic value to digital content,said method comprising the steps of uploading a digital content by auser; selecting at least one condition from a plurality of conditionsthat the uploaded digital content is intended to cure, said selectedcondition indicating at least one of a feeling, sensation, mood, mentalstate, physical state, emotional condition, physical status; andoverlaying a therapeutic label to the digital content corresponding tothe selected condition.

It is another object to disclose and claim a method and system, whereinsaid system comprises of a condition selector, a therapeutic labeler, anon-transitory storage element coupled to the processor wherein theencoded instructions when implemented by the processor, configure thedigital therapeutic value pipeline to: upload a digital content by auser; select at least one condition from a plurality of conditions thatthe uploaded digital content is intended to cure by the conditionselector, said selected condition indicating at least one of a feeling,sensation, mood, mental state, physical state, emotional condition,physical status; and overlay a therapeutic label to the digital contentcorresponding to the selected condition by the therapeutic labeler.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a graphical representation of one embodiment of the electronicmessaging system.

FIG. 2 is a graphical representation of one embodiment of the electronicmessaging system.

FIGS. 3A and 3B are graphical representations of one embodiment of theelectronic messaging system.

FIGS. 4A, 4B, 4C and 4D are graphical representations of one embodimentof the electronic messaging system.

FIGS. 5A, 5B and 5C are graphical representations of one embodiment ofthe electronic messaging method.

FIG. 6 is a graphical representation of one embodiment of the electronicmessaging method.

FIGS. 7A and 7B are graphical representations of one embodiment of theelectronic messaging system.

FIGS. 8A, 8B, 8C, and 8D is a graphical representation of one embodimentof the electronic messaging system. are flow diagrams of one embodimentof the electronic messaging system.

FIG. 9 illustrates a network diagram in accordance with an aspect of theinvention.

FIG. 10 illustrates a block diagram depicting the digital therapeuticsystem in accordance with an aspect of the invention.

FIG. 11 illustrates a block diagram depicting the digital therapeuticsystem in accordance with an aspect of the invention.

FIG. 12 illustrates a flow diagram depicting the digital therapeuticmethod in accordance with an aspect of the invention.

FIG. 13 illustrates a representative screen shot depicting an exemplaryuser interface in accordance with an aspect of the invention.

FIG. 14 illustrates a representative screen shot depicting an exemplaryuser interface in accordance with an aspect of the invention.

FIG. 15 illustrates a representative screen shot depicting an exemplaryuser interface in accordance with an aspect of the invention.

FIG. 16 illustrates a representative method flow of the therapeuticlabeler in accordance with an aspect of the invention.

FIG. 17 illustrates a representative block diagram of the therapeuticlabeler system in accordance with an aspect of the invention.

FIG. 18 illustrates a representative interaction flow of the therapeuticlabeler system in accordance with an aspect of the invention.

FIG. 19 illustrates a representative screen shot of an initiatingsequence of the therapeutic labeler system in accordance with an aspectof the invention.

FIG. 20 illustrates a representative screen shot of a therapeutic labeloverlaid on a digital content in accordance with an aspect of theinvention.

FIG. 21 illustrates a representative screen shot of a more detailedtherapeutic label overlaid on a digital content in accordance with anaspect of the invention.

FIG. 22 illustrates a quick reference guide of the therapeutic labelerin accordance with an aspect of the invention.

FIG. 23 illustrates a quick reference guide of the therapeutic labelerin accordance with an aspect of the invention.

FIG. 24 illustrates a flow diagram depicting the digital therapeuticlabeling method in accordance with an aspect of the invention.

FIG. 25 illustrates a system block diagram of the digital therapeuticlabeling system in accordance with an aspect of the invention.

DETAILED DESCRIPTION OF DRAWINGS

Numerous embodiments of the invention will now be described in detailwith reference to the accompanying figures. The following description ofthe embodiments of the invention is not intended to limit the inventionto these embodiments but rather to enable a person skilled in the art tomake and use this invention. Variations, configurations,implementations, and applications described herein are optional and notexclusive to the variations, configurations, implementations, andapplications they describe. The invention described herein can includeany and all permutations of these variations, configurations,implementations, and applications.

In the following description, numerous specific details are set forth inorder to provide a thorough understanding of the invention. It will beapparent, however, to one skilled in the art that the invention can bepracticed without these specific details.

Reference in this specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentof the invention. The appearances of the phrase “in one embodiment” invarious places in the specification are not necessarily all referring tothe same embodiment, nor are separate or alternative embodimentsmutually exclusive of other embodiments. Moreover, various features aredescribed which may be exhibited by some embodiments and not by others.Similarly, various requirements are described which may be requirementsfor some embodiments but no other embodiments.

FIG. 1 depicts a schematic of a system 100 for imposing a dynamicsentiment vector to an electronic message. In one embodiment, a system100 can include: a sentiment vector generator 110, a processor 120, andan electronic computing device 140 associated with a particular user130. The sentiment vector generator 110, the processor 120, and theelectronic computing device 140 are communicatively coupled via acommunication network. The network may be any class of wired or wirelessnetwork including any software, hardware, or computer applications thatcan provide a medium to exchange signals or data. The network may be alocal, regional, or global communication network.

The electronic computing device 140 may be any electronic device capableof sending, receiving, and processing information. Examples of thecomputing device include, but are not limited to, a smartphone, a mobiledevice/phone, a Personal Digital Assistant (PDA), a computer, aworkstation, a notebook, a mainframe computer, a laptop, a tablet, asmart watch, an internet appliance and any equivalent device capable ofprocessing, sending and receiving data. The electronic computing device140 can include any number of sensors or components configured to intakeor gather data from a user of the electronic computing device 140including, but not limited to, a camera, a heart rate monitor, atemperature sensor, an accelerometer, a microphone, and a gyroscope. Theelectronic computing device 140 can also include an input device (e.g.,a touchscreen or a keyboard) through which a user may input text andcommands.

As further described below, the sentiment vector generator 110 isconfigured to receive an electronic message 160 (e.g., a text input)from the particular user 130 associated with the electronic computingdevice 140 and run a program 116 executed by the processor 120 toanalyze contents of the electronic message, determine a tone or asentiment that the particular user 130 is expressing through theelectronic message 160, and apply a sentiment vector to the electronicmessage 160, the sentiment vector designed to convey the tone orsentiment determined by the sentiment vector generator 110. Theelectronic message 160 can be in the form of a SMS message, a textmessage, an e-mail, a social media post, an enterprise-level workflowautomation tool message, or any other form of electronic, text-basedcommunication. The electronic message 160 may also be a transcription ofa voice message generated by the particular user 130. For example, inone embodiment, from a messaging application installed on the electroniccomputing device 140, the user 130 may select to input a voice (i.e.,audio) message through a microphone coupled to the electronic computingdevice 140 or initiate a voice message through a lift-to-talk feature(e.g., the user lifts a mobile phone to the user's ear and the messagingapplication automatically begins recording a voice message). In thisexample, the system 100 can generate a transcription of the voicemessage or receive a transcription of the voice message from themessaging application. After receiving or generating the transcription(i.e., an electronic message), the sentiment vector generator 110 canthen analyze the message content within the electronic message,determine the mood or sentiment of the message content, and apply acorresponding sentiment vector to the electronic message, as furtherdescribed below.

In one embodiment, the system 100 may receive an electronic message 160in the form of an electroencephalograph (EEG) output. For example, inthis embodiment, a user can generate a message using an electronicdevice communicatively coupled to the user and capable of performing anelectroencephalograph to measure and record the electrochemical activityin the user's brain. In this example, the system 100 can transcribe theEEG output into an electronic message 160 or receive a transcription ofthe EEG output from the electronic device communicatively coupled to theuser. After receiving or generating the electronic message 160 from theEEG, the sentiment vector generator 110 can then analyze the messagecontent within the electronic message 160, determine the mood orsentiment of the message content, and apply a corresponding sentimentvector to the electronic message. In one example of this embodiment, auser is connected to an augmented reality (AR) or virtual reality (VR)headset capable of performing an EEG or an equivalent brain mappingtechnique. The user can generate a message simply by thinking of whatthe user is feeling or would like to say. The headset can monitor andrecord these thoughts and feelings using the EEG and transcribe thethoughts and feelings into an electronic message or send the EEG outputsignals directly to the system 100. The system 100 can then analyze themessage content included within the electronic message 160, determinethe mood or sentiment of the message content, and apply a correspondingsentiment vector to the electronic message 160, creating a vectorizedmessage. The system 100 can then send the vectorized message to theuser's intended recipient (e.g., a recipient that the user thought of).

In one embodiment, the particular user 130 may submit an electronicmessage 160 through a mobile application (e.g., a native or destinationapp, or a mobile web application) installed on the particular user'smobile phone or accessed through a web browser installed on the user'sphone. In one example of this embodiment, the user accesses the mobileapplication, submits the electronic message 160 in the form of a textinput. The sentiment vector generator 110 can then analyze the messagecontent included within the electronic message 160, determine the moodor sentiment of the message content, and apply a corresponding sentimentvector to the electronic message 160, creating a vectorized message. Inthis example, the user can then send the vectorized message to theuser's intended recipient(s) 131 (e.g., by copying and pasting thevectorized message into a separate messaging application or selecting toexport the vectorized message to a separate application, as furtherdescribed below). In one variation of this embodiment, the user may sendthe vectorized message to the intended recipient 131 directly throughthe mobile application. In one embodiment, the user may submit anelectronic message 160, or a component of an electronic message (e.g., asingle word or phrase within the message content of an electronicmessage) using a touch input gesture. In one example of this embodiment,the user may submit the electronic message 160 through an electroniccomputing device by swiping a finger on a touch screen coupled to theelectronic computing device 140 in a U-shaped gesture on the electronicmessage.

In another embodiment, the user may input an electronic message 160 intoan entry field of a third-party application such as an email client(e.g., Gmail, Yahoo Mail) or a social media application (e.g., Facebook,Twitter, Instagram). For example, the user may input a message into thebody of an email, or into a status update on Facebook. In thisembodiment, the system 100 can detect the input of the electronicmessage 160 into the third-party application and upload the electronicmessage 160 to the sentiment vector generator 110. The sentiment vectorgenerator 110 can then analyze the message content contained within theelectronic message 160, determine the mood or sentiment of the messagecontent, and apply a corresponding sentiment vector to the electronicmessage 160, creating a vectorized message. The sentiment vector 110 canthen replace the electronic message 160 within the third-partyapplication with the vectorized message. Alternatively, the user mayselect to replace the electronic message 160 with the vectorized message(e.g., by copying and pasting the vectorized message into the entryfield).

FIG. 2 depicts a [schematic] of the sentiment vector generator 110. Inone embodiment, the sentiment vector generator 110 includes a parsingmodule 112, a dynamic sentiment value spectrum 114, a program 116, and alibrary of sentiment vectors. In this embodiment, after receiving anelectronic message 160, the sentiment vector generator 110 can activatethe program 116 executed by a processor 120 to analyze message contentcontained within the electronic message 160 using the parsing module112, the sentiment value spectrum 114, and the library of sentimentvectors, which are discussed in further detail below. Part or all of thesentiment vector generator 110 may be housed within the electroniccomputing device 140. Likewise, part of all of the sentiment vectorgenerator 110 may be housed within a cloud computing network.

FIG. 3 depicts a schematic of the parsing module 112. The parsing module112 is configured to parse message content contained within anelectronic message 160 received by the sentiment vector generator 110for emotionally-charged language and determine a sentiment value for theelectronic message 160 from the dynamic sentiment value spectrum 114. Inone embodiment, the parsing module 112 can include one or both of aheuristic layer 112 a and a semantic layer 112 b. The heuristic layer112 a is configured to recognize, within the message content containedwithin the electronic message 160, shorthand script, symbols, andemotional icons (emoticons). For example, the message “r u okay? :(”contains the shorthand character “r” to represent the word “are,” theshorthand character “u” to represent the word “you,” and the emoticon“:(,” representing an unhappy face, each of which the heuristic layer112 a is configured to recognize. The heuristic layer 112 a can befurther configured to translate recognized shorthand script, symbols,and emoticons into a standardized lexicon. For example, referring backto the previous example, the heuristic layer can translate “u” into“you,” “r” into “are,” and “:(” into “[sad].” The heuristic layer 112 acan thus translate the entire message from “r u okay? :(” to “are youokay? [sad]” in order to compare the sentiments expressed withindifferent messages in a more objective manner and determine the natureof the emotionally-charged language contained within the message ofcontent of the electronic message 160.

The semantic layer 112 b is configured to recognize, within the messagecontent contained within the electronic message 160, natural languagesyntax. For example, in the message “is it ok if we text on WhatsApp ?”the construction of the phrases “is it ok” and “WhatsApp ?” reflectnatural language syntax that can express particular sentiments. “is itok[?]” can express tentativeness in addition to the objective questionthat the phrase asks. For reference, inverting and contracting the firsttwo words to create the phrase “it's okay[?]” results in a phrase thatcan express more confidence. Likewise, the space inserted between“WhatsApp” and “?” can have the effect of “softening” the question markin comparison to “WhatsApp?” The semantic layer 112 b is configured torecognize the use of natural language syntax such as “is it ok” and“WhatsApp ?” and can be further configured to translate the recognizednatural language syntax into a standardized lexicon. The standardizedlexicon can be a standard set of words and terms (e.g., an Oxforddictionary) that the parsing module 112 is able to parse foremotionally-charged language. In one embodiment, the standardizedlexicon is a standard set of words and terms with predefined attributes.For example, again referring to the previous example, the semantic layer112 b can translate the entire message from “is it ok if we text onWhatsApp ?” to “can[soft] we text on WhatsApp?[soft]” in order tocompare the sentiments expressed within different messages in a moreobjective manner and determine the nature of the emotionally-chargedlanguage contained within the message of content of the electronicmessage 160.

In one embodiment, the parsing module 112 can include a library ofemotionally-charged language 112 c. In this embodiment, after parsingthe message content contained within the electronic message 160, theparsing module 112 can cross-reference the words and terms containedwith the message content to the library of emotionally-charged language112 c. The words and terms contained within the library ofemotionally-charged language 112 c may be tagged with attributesaccording to the sentiments they most commonly express. For example, thelibrary of emotionally-charged language 112 c may include the terms“disastrous,” “splendid,” “terrible,” and “awesome.” Within the libraryof emotionally-charged language 112 c, “disastrous” may be tagged withthe attribute [bad] or [negative]; “splendid” may be tagged with theattribute [good] or [positive]. In one embodiment, the terms containedwithin the library of emotionally-charged language 112 c mayadditionally or alternatively be tagged with a numeric value. Forexample, “disastrous” may be tagged with the attributes [negative; 7],and “terrible” may be tagged with the attributes [negative; 5],indicating that while “disastrous” and “terrible” may express similar“negative” sentiments, “disastrous” is more negative than “terrible.” Inone embodiment, the parsing module 112 (or, alternatively, any componentof the system 100) can dynamically add or remove words or terms to andfrom the library of emotionally-charged language 112 c. The parsingmodule 112 may use any technique to tag or evaluate the sentiments ofemotionally-charged language.

In one embodiment, the library of emotionally-charged language 112 c isspecific to the particular user 130. In this embodiment, each particularuser 130 of the system 100 access a unique library ofemotionally-charged language 112 c associated only with that particularuser. In one variation of this embodiment, the particular user 130 maymanually add or remove words and terms to and from the library ofemotionally-charged language 112 c. In one embodiment of the system 100,the system 100 can be accessed by multiple users. In one variation ofthis embodiment, the library of emotionally-charged language 112 cemployed by the parsing module 112 is the same for each user.

In one embodiment of the system 100, the parsing module additionallyincludes a neural network 150 and a library of inputs 151. In thisembodiment, after parsing the message content of an electronic message160 received by the sentiment vector generator 11, the parsing module112 can store the electronic message 160 in the library of inputs 151,along with the emotionally-charged language found within the messagecontent and any accompanying attributes, creating a database of messagesand their accompanying emotionally-charged language. In this embodiment,the neural network 150 can employ machine learning techniques to analyzethis database for patterns and trends in order to dynamically improvethe performance of the sentiment vector generator 110. For example, theneural network 150 may determine through the application of an algorithmthat the particular user 130 uses the term “disastrous” ten times moreoften than the particular user 130 uses the term “terrible.” Thus, eventhough “disastrous” may be a more negative term than “terrible” for theaverage user or person, the neural network can determine that, for theparticular user 130, “disastrous” generally carries less emotionalweight than “terrible.” In this example, the neural network 150 can thenupdate the parsing module 112 and the library of emotionally-chargedlanguage accordingly. For example, in the example in which the terms“disastrous” and “terrible” begin as tagged within the library ofemotionally-charged language 112 c as [negative; 7] and [negative; 5],respectively, the neural network 150 can update the attributes to read[negative; 5] and [negative 7], respectively. In one embodiment, theparsing module 112 can store electronic messages into the library ofinputs 151 along with their standardized lexicon conversions.

FIG. 4 depicts graphical representations of the parsing of electronicmessages by the parsing module 112. FIG. 4A depicts the parsing of threeseparate electronic messages 160, “it definitely has given me more timeand flexibility and channels creativity differently” 160 a, “is it ok ifwe text on WhatsApp ?” 160 b, and “Oh u live in Williamsburg” 160 c foremotionally-charged language by the parsing module 112. In, thisexample, in the message content of 160 a, the parsing module 112determines three emotionally-charged words and terms: “definitely has,”“and,” and “differently;” in the message content of 160 b: “ok,” “we,”and “WhatsApp ?” and in the message content of 160 c: “u” and“Williamsburg.” In one embodiment, as discussed above, after parsing themessage content, the parsing module 112 can determine attributes for theemotionally-charged language found in the message content, as depictedby S123 in FIG. 4B. In the example depicted in FIG. 4B, the parsingmodule 112 tags “definitely has” with [positive, active], “and” with[neutral], and “differently” with [negative]. In one embodiment, asdiscussed above, the parsing module 112 includes a semantic layer 112 bconfigured to recognize, within the message content contained within theelectronic message 160, natural language syntax, as depicted by S122 inFIG. 4B. In the example depicted in FIG. 4B, the semantic layer 112 brecognizes the space between “WhatsApp” and “?” in “is it ok if we texton WhatsApp?” as an instance of natural language syntax. In oneembodiment, as discussed above, the parsing module 112 includes aheuristic layer 112 a configured to recognize, within the messagecontent contained within the electronic message 160, shorthand script,symbols, and emoticons, as depicted by S124 in FIG. 4B. In the exampledepicted in FIG. 4B, the heuristic layer 112 a recognizes “u” as ashorthand term for “you.”

In one embodiment, as discussed above, after parsing the message contentcontained within the electronic message 160, the parsing module 112 cancross-reference the words and terms contained with the message contentto a library of emotionally-charged language 112 c, as depicted in FIG.4C. In the example depicted in FIG. 4C, the parsing module 112cross-references electronic message 160 a with the library ofemotionally-charged language 112 c and determines that “differently,”“more,” “flexibility,” and “differently” are emotionally-charged wordsor terms. In one embodiment, as discussed above, before parsing themessage content of an electronic message 160, the parsing module 112 canconvert the message content into a standardized lexicon, as depicted inFIG. 4D. In the example depicted in FIG. 4D, the parsing module 112converts “is it ok if we text on WhatsApp?” into the converted text, “isit okay if we text on WhatsApp?” in step S126 before parsing theconverted text for emotionally-charged language in step S128.

FIGS. 5A, 5B, and 5C depict a graphical representation of a dynamicsentiment value spectrum 114. In one embodiment, after parsing messagecontent of an electronic message 160 for emotionally-charged language,the sentiment vector generator 110 can generate a sentiment value from adynamic sentiment value spectrum 114 for the electronic message 160. Inone variation of this embodiment, the dynamic sentiment value spectrum114 can be represented as a coordinate system, as depicted in FIG. 5A.In the example depicted in FIG. 5A, the dynamic sentiment value spectrum114 is a Cartesian coordinate system consisting of two axes: ahorizontal axis 115 a ranging from positive to negative (henceforth, thepositivity axis) and a vertical axis 115 b ranging from passive toactive (henceforth, the activity axis). In this example, the dynamicsentiment value spectrum 114 consists of a multitude of differentsentiments, each occupying a different position on the coordinatesystem. For example, the sentiments “Happy,” “Astonished,” and“Inquisitive” (114 a-114 c, respectively) all occupy the second quadrantof the coordinate system, defined by a positive position on thepositivity scale and an active position on the activity scale (i.e.,each of these sentiments are determined by the sentiment vectorgenerator 110 to be positive and active sentiments). In this example,the sentiment vector generator considers Inquisitive 114 c to be a moreactive but less positive sentiment than Astonished 114 b and Astonishedto be a less positive and less active sentiment than Happy 114 a. Also,in this example, the sentiments “Shocked,” “Sad,” and “Mad” (114 d-114f, respectively) all occupy the first quadrant of the coordinate system,defined by a negative position on the positivity scale and an activeposition on the activity scale (i.e., each of these sentiments aredetermined by the sentiment vector generator to be active and negativesentiments). However, the dynamic sentiment value spectrum 114 need notbe a coordinate system. Rather, the dynamic sentiment value spectrum 114may take on any appropriate form (e.g., a list, a linear scale, etc.).Additionally, the sentiment value spectrum does not need to be dynamic.

In one embodiment, as discussed above, after parsing message contentcontained within an electronic message 160 for emotionally-chargedlanguage, the parsing module 112 can assign attributes to theemotionally-charged language found in the message content of theelectronic message 160. In one embodiment, the sentiment vectorgenerator 110 can analyze the emotionally-language and theiraccompanying attributes to generate a sentiment value from the dynamicsentiment value spectrum 114, as depicted in FIG. 5B. For example, inthe example depicted in FIG. 5B, the parsing module 112 can assign eachemotionally-charged term found in the message content of an electronicmessage with respective coordinate values on the positivity and activityaxes of the Cartesian coordinate dynamic sentiment value spectrumdiscussed in the example above. In this example, the sentiment vectorgenerator 110 can take the coordinate position of eachemotionally-charged term, calculate an average position of theemotionally-charged terms, and plot the average positon on the dynamicsentiment value spectrum 114 depicted in FIG. 5A. Then, in this example,the sentiment vector generator 110 can generate a sentiment value forthe electronic message by determining the sentiment value on the dynamicsentiment value spectrum 114 closest to the average position of theemotionally-charged terms.

In one embodiment, the sentiment vector generator 110 can generate asentiment value for an electronic message 160 by determining which ofthe emotionally-charged terms found in the message content of theelectronic message carries the most emotional weight. For example, inone embodiment, the parsing module 112 can parse the message content ofan electronic message 160 for emotionally-charged language and assigneach emotionally-charged term with a positivity scale value, an activityscale value, and an emotional weight value. In this embodiment, thesentiment vector generator 110 can then determine a sentiment value forthe electronic message by determining which of the emotionally-chargedterms has the highest emotional weight value, and then determining thesentiment value on the dynamic sentiment value spectrum 114 closest tothe position of emotionally-charged term with the highest emotionalweight value.

In one embodiment, the library of emotionally-charged language 112 cassociates each emotionally-charged term contained within the librarywith a sentiment value from the dynamic sentiment value spectrum 114.For example, the library of emotionally-charged language 112 c mayassociate the words “gleeful,” “splendid,” and “terrific” with a “happy”sentiment value. In this example, if the message content of anelectronic message 160 includes any of the terms “gleeful,” “splendid,”or “terrific,” the sentiment vector generator 110 can generate a “happy”sentiment value for the electronic message 160. However, the sentimentvector generator can generate a sentiment value for an electronicmessage 160 using any other methodology.

In one embodiment, the particular user 130 may select a sentiment valuefrom the dynamic sentiment value spectrum for an electronic message 160.In one variation of this embodiment, after the parsing module 112 parsesthe message content of an electronic message 160 submitted by theparticular user 130, the sentiment vector generator 110 can generatemultiple sentiment values for the electronic message 160 and present themultiple sentiment values for the electronic message 160 to theparticular user 130 for selection. For example, after receivingelectronic message 160 a (depicted in FIG. 4A), the sentiment vectorgenerator 110 may generate an “excited” sentiment value and a“melancholy” sentiment value for electronic message 160 a. In thisexample, the particular user 130 may be given the choice to pick betweenthe “excited” sentiment value and the “melancholy” sentiment value, inorder to further ensure that the proper (i.e., intended) sentiment willbe expressed.

In one embodiment, as discussed above, the system 100 includes a neuralnetwork 150 and a library of inputs 151 communicatively coupled to thesentiment vector generator 110. In one variation of this embodiment,after generating a sentiment value for an electronic message 160, thesentiment vector generator 110 store the electronic message 160 and itsaccompanying sentiment value in the library of inputs 151 creating adatabase of messages and their accompanying sentiment values. In thisembodiment, the neural network 150 can employ machine learningtechniques to analyze this database for patterns and trends in order todynamically improve the performance of the sentiment vector generator110. In one variation of this embodiment, the neural network 150 candynamically edit or rearrange the dynamic sentiment value spectrum 114.In the rearranged version, the sentiment values have adjusted andcoalesced into more discrete sections (115 c-115 e). This may reflectthat a particular user 130 associated with the rearranged sentimentvalue spectrum 117 generates messages most of their messages with asimilar tone, making the difference between similar sentiments subtlerthan that of the average person.

In one embodiment, the sentiment vector generator 110 can generate asentiment value for an electronic message 160 at least in part byutilizing information about a particular user 130. For example, in oneembodiment, the system 100 can generate sender context associated with aparticular user 130. The sender context can include, but is not limitedto: social media data associated with the particular user, data obtainedfrom IoT (internet of things) devices associated with the particularuser, data obtained from wearable devices associated with the particularuser, genetic profile data associated with the particular user, andstress data of the particular user. In one variation of this embodiment,the system 100 can leverage sensors and inputs coupled to an electroniccomputing device 140 associated with the particular user 130 to generatesender context associated with the particular user 130, as depicted bystep S160 in FIG. 6. For example, in the example depicted in FIG. 6, thesystem 100 can leverage a camera built into a mobile phone associatedwith the particular user 130 to capture images of the face of theparticular user. In this example, the system 100 can then analyze theimages of the face of the user (e.g., the eye motion or lip curvature ofthe user) and determine the mood of the user at the time that theelectronic message 160 is generated. The sentiment vector generator 110can then generate a sentiment value using the determined mood of theuser. In one variation of this embodiment, the system 100 can leveragesensors coupled to wearable devices associated with a particular user,such as a smart watch, intelligent contact lenses, or cochlear implants.For example, the system 100 can leverage a microphone built into acochlear implant to capture the heartrate of a user at the time that theuser is generating an electronic message 160. Using the capturedheartrate, the sentiment vector generator 110 can then determine astress level of the user at the time that the user generated theelectronic message 160 and generate a sentiment value using thedetermined stress level of the user. Sender context can additionally oralternatively include: facial expression, motion or gesture, respirationrate, heart rate, and cortisol level.

In another variation of the previous embodiment, the sentiment vectorgenerator 110 can generate a sentiment value for an electronic message160 at least in part by utilizing information about an intendedrecipient of the electronic message 160. In this embodiment, afterreceiving an electronic message 160, the system 100 can determine anintended recipient 131 of the electronic message 160. The system 100 canthen generate recipient context associated with the intended recipient131. The recipient context can include but is not limited to: socialmedia data associated with the intended recipient, data obtained fromIoT (internet of things, e.g., a smart home assistant such the AmazonEcho) devices associated with the intended recipient, data obtained fromwearable devices associated with the intended recipient, genetic profiledata associated with the intended recipient, and stress data associatedwith the intended recipient. For example, in one embodiment, the system100 can leverage sensors built into an electronic device 141 associatedwith the intended recipient to determine a mood of the intendedrecipient 131 at the time that the electronic message 160 is generated.The sentiment vector generator 110 can then generate a sentiment valuefor the electronic message 160 based at least in part on the determinedmood of the intended recipient 131.

After generating a sentiment value for an electronic message 160, thesentiment vector generator 110 can then select a sentiment vector from alibrary of sentiment vectors 118, the selected sentiment vector designedto convey a sentiment corresponding to the generated sentiment value,and impose the selected sentiment vector to the electronic message 160,as depicted in FIG. 7. The library of sentiment vectors 118 can includebut is not limited to: a color change of a component of the messagecontent, a change in the text font of a component of the messagecontent, an audio effect, a haptic effect, and a graphical addition tothe message content. For example, in one embodiment, after generating a“mad” sentiment value, the sentiment vector generator 110 may change thebackground of the electronic message 160, as depicted by step S141 a inFIG. 7A, such as changing the background of the electronic message 160to red to reflect the mad sentiment. Or, for example, in one variationof this embodiment, the sentiment vector generator 110 may opt tohighlight only key words or terms in red, or change the fonts of keywords or terms to red. The sentiment vector generator 110 can impose anysort of color change to the electronic message 160 in order to convey acorresponding sentiment.

In one embodiment, for example, after generating an “inquisitive”sentiment value for an electronic message 160, the sentiment vectorgenerator 110 may impose a graphic onto the electronic message 160, asdepicted by step 141 b in FIG. 7A, such as adding question mark graphicsto the background of the electronic message 160. In one variation ofthis example, the sentiment vector generator 110 can add one questionmark to the end of the message content of the electronic message 160 ina font size that is larger than the font size of the rest of the messagecontent. In another variation of this example, the sentiment vectorgenerator 110 may impose a .gif file to the background of electronicmessage 160, in which one question mark grows and shrinks in periodicintervals. The sentiment vector generator 110 can impose any sort ofstatic or dynamic graphic to the electronic message 160 in order toconvey a corresponding sentiment.

In one embodiment, for another example, after generating a “judgmental”sentiment value for an electronic message 160, the sentiment vectorgenerator 110 can edit the font of a key word in the message content, asdepicted by step S141 c in FIG. 7A, such as italicizing one of the wordscontained in the message content. Such font effects can include, but arenot limited to, italicizing the font, changing the size of the font,bolding, underlining, and changing the spacing between characters,words, and lines. The sentiment vector generator 110 can impose any sortof font change to the electronic message 160 in order to convey acorresponding sentiment.

In one embodiment, the sentiment vector generator 110 can impose ananimated character or personality to the electronic message 160, ortranspose the electronic message 160 into a graphic of an animatedcharacter or personality. For example, in one variation of thisembodiment, the library of sentiment vectors 118 may include a series ofthe same animated character (take, for example, an animated llama orchicken) performing various actions associated with variouscorresponding sentiments. For example, the library of sentiment vectors118 may include a static or dynamic graphic of an animated chickenstomping with red eyes (expressing anger), another graphic of theanimated chicken laying in a hammock and basking in the sun (expressingcontentedness), and another graphic of the animated chicken blowing akiss (expressing affection). In this example, after generating an“anger” sentiment value for an electronic message 160, the sentimentvector generator 110 can transpose the electronic message into thegraphic of the animated chicken stomping and saying the message contentof the electronic message 160.

In one embodiment, the sentiment vector generator 110 can impose ahaptic effect onto an electronic message 160. For example, aftergenerating an “anger” sentiment value for an electronic message 160, thesentiment vector generator 110 can impose a vibration or vibrationpattern onto the electronic message 160, as depicted by step S141 d inFIG. 7B, such as three short vibrations. In another example, aftergenerating a “contented” sentiment value for an electronic message 160,the sentiment vector generator 110 can impose one long and mutedvibration to the electronic message 160. The sentiment vector generator110 can impose any form of vibration or vibration pattern to anelectronic message in order to convey a corresponding sentiment.

In one embodiment, the sentiment vector generator 110 can impose anaudio effect onto an electronic message 160. For example, aftergenerating an “unhappy” sentiment value for an electronic message 160,the sentiment vector generator 110 can impose an audio accompanimentonto the electronic message 160, as depicted by step S142 in FIG. 7B,such as protracted “noon.” In another example, the sentiment vectorgenerator 110 can impose a voice accompaniment dictating the messagecontent of the electronic message 160 and stressing key words containedwithin the message content. The voice accompaniment may stress key wordscontained within the message content in any number of ways including,but not limited to: increasing or decreasing in volume, changing theintonation of the voice, changing the speed of the voice, or changingthe cadence of the voice accompaniment. In one embodiment, the voiceaccompaniment vector may be a recorded and processed version of theparticular user's voice. In one embodiment, the voice accompanimentvector may be the voice of another individual, such as a celebrity, or acombination of the particular user's voice and the voice of anotherindividual.

In one embodiment, after generating a sentiment value for an electronicmessage 160, the sentiment vector generator 110 can impose a vector ontothe electronic message 160 that adjusts the position of the wordscontained with the message content of the electronic message, asdepicted by step S141 e in FIG. 7B. In one variation of this embodiment,the adjustment of the words contained within the message content isstatic, such that the words occupy new positions in a static image. Inone variation of this embodiment, the adjustment of the words containedwithin the message content is dynamic, such that the words containedwithin the message content move within the resulting vectorized message.

In one embodiment, a user may submit sentiment vectors to the sentimentvector generator 110. For example, in one embodiment, a user may submita picture or graphic design to impose onto the background of anelectronic message and select a sentiment value for the picture orgraphic design to be associated with. In this example, after generatinga sentiment value for an electronic message 160 corresponding to thesentiment value that the user has selected to associate with the pictureor graphic design, the sentiment vector generator 110 can impose thepicture or graphic design to the background of the electronic message160 to convey the corresponding sentiment. In another example, in onevariation of this embodiment, a user can select a sentiment vectorpreviously included in the library of sentiment vectors 118 andpreviously associated with a sentiment value and disassociate thesentiment vector from the associated sentiment value, or re-associatethe sentiment vector with a different sentiment value. In yet anotherexample, in one variation of this embodiment, a user can select one ormore elements from existing sentiment vectors contained within thelibrary of sentiment vectors 118 and combine them to create a newsentiment vector. In this example, the user can also choose a sentimentvalue to associate with the new sentiment vector. In another example, inone variation of this embodiment, a user can select a sentiment vectorby scrolling through a list of sentiment vectors (e.g., a list includingoptions to adjust text weight, height, font, color, highlight, orcontent animation) using a flicking gesture, within a mobileapplication, on a touch screen coupled to an electronic computingdevice.

The sentiment vector generator can include or generate, but is notlimited to, sentiment vectors using any combination of the elements ofthe sentiment vectors described herein. Additionally, environmentalconditions and factors for example, but not limited to, wind, heat,humidity, cold may also play a role in generating the sentiment vector.

In one embodiment of the system 100, a user can submit an electronicmessage 160 to the sentiment vector generator 110 through a mobileapplication (e.g., a native application), as discussed above. In onevariation of this embodiment, the mobile application can storevectorized messages generated by the sentiment vector generator andallow the user to search through the vectorized messages. In thisembodiment, the user can search through the vectorized messages usingdifferent filters or queries including, but not limited to: mood, color,content, and sentiment. For example, in one embodiment, the user canenter a sentiment as “anger” as a search query, and a graphical userinterface of the mobile application can display a list of all of thevectorized messages that the user has created through the sentimentvector generator 110 with a sentiment value corresponding to an “anger”sentiment. In one embodiment, the sentiment vector generator 110 canimpose a hyperlink onto an electronic message 160. FIGS. 8A, 8B, 8C, and8D are flow diagrams of one embodiment of the electronic messagingsystem.

In an embodiment of the invention, the sentiment vector generator 110can impose a hyperlink onto an electronic message 160. An imperativefunction of the sentiment vector is GEEQ (genetics, emotion andelectroencephalography) and its capacity to integrate messages andmessaging with movement and thought as well as the ability to pairinformation with form and performative elements. In a nutshell, ourtechnology will introduce, integrate, account for, and actively utilizeGEEQ (Genetics, Emotion, and Electroencephalography). GEEQ, by its verydesgn, integrates and intermingles the beliefs and postulates of Darwin,Mendel, Mendelssohn, Morgan, and Martha Graham.

FIG. 9 illustrates a network diagram of the digital therapeutic systemin accordance with an aspect of the invention. As shown, at least oneprocessor 204 is connected to the Internet (network) 206 via either awireless (e.g. WiFi link) or wired link to an Internet connected router,usually via firewall. The network 206 may be any class of wired orwireless network including any software, hardware, or computerapplications that can provide a medium to exchange signals or data. Thenetwork 206 may be a local, regional, or global communication network.Various servers 204, such as a remote VCS Internet server, andassociated database memory can connect with the at least a user device(1 . . . n). Additionally, various user devices (e.g. Smartphones,tablet computers, laptop computers, desktop computers and the like) canalso connect to both the processor-controlled IoT hubs, sensors disposedon the device configured for data gathering, and/or the remote VCSInternet server 204.

As will be discussed, often a plurality of different user devices may beused, but for simplicity this plurality of devices will often be spokenof in the singular form. This use of the singular form is not intendedto be limiting, and in general the claims and invention should beunderstood as operating with a plurality of devices. Although forsimplicity, often mobile client computerized devices such as Internetconnected versions of the popular Android, iOS, or Windows smartphonesand tablets will be used as specific examples of devices, these specificexamples are not intended to be limiting. The electronic computingdevice may include any number of sensors or components configured tointake or gather data from a user of the electronic computing deviceincluding, but not limited to, a camera, a heart rate monitor, atemperature sensor, an accelerometer, a microphone, and a gyroscope. Theelectronic computing device can also include an input device (e.g., atouchscreen or a keyboard) through which a user may input text andcommands.

While not shown, note that server, Internet connected storage device anddatabase memory may all be located in the cloud. This is intended toboth designate and remind the reader that the server, Internet connectedstorage device and database memory are in fact operating according toscalable Internet cloud-based methods that in turn operate according toautomated service provisioning and automated virtual machine migrationmethods. As previously discussed, examples of such scalable methodsinclude, but are not limited to, Amazon EC2, Microsoft Windows Azureplatform, and the Google App Engine. Thus, for example, server andInternet connected storage device will often be implemented asautomatically provisioned virtual machines under a cloud service systemthat can create a greater or lesser number of copies of server andInternet connected video storage device and associated database memoryaccording to the underlying demands on the system at any given time.

Preferred embodiments may include the addition of a remote server 204 orcloud server to further provide for back-end functionality and support.Any one of the storage or processing may be done on-board the device orbe situated adjacent or remotely from the system and connected to eachsystem via a communication network 206. In one embodiment, the server204 may be used to support user behavior profiling; user historyfunction; predictive learning/analytics; alert function; network sharingfunction; digital footprint tracking, etc. The remote server 204 may befurther configured to authenticate the user and retrieve data of theuser, device, and, or network and applies the data against a library ofmessages, content, validated user information, etc.

Now in reference to FIGS. 10 and 11. FIGS. 10 and 11 both illustrate anexemplary embodiment of the digital therapeutic delivery system. FIGS.10 and 11 illustrate an exemplary processing unit with at least a oneprescriber 305, 307 configured for displaying interactively therapeuticcontent from an EMS store 303, 403 based on a user-specific EMS. Asshown, the system may comprise an EMS store 303, 403; at least a primarymessage prescriber 305; a processor coupled to a memory element withinstructions, the processor when executing said memory-storedinstructions, configure the system to cause: at least one EMS from aplurality of EMS in the EMS store 303, 403 to be selected by the user.

As shown in FIG. 11, any number of EMS or EMS types may be included inthe EMS store 303, 403. Each EMS may indicate at least one of a feeling,sensation, type of discomfort, mood, mental state, emotional condition,physical status of the user, and, or a behavioral intervention ortraining regimen. FIG. 11 also illustrates the fact that any number ofmessages or interactively therapeutic content may be associated witheach EMS type. Each message; or interactively therapeutic content; orpushed therapeutic may contain at least one of a text, image, sound,video, art asset, suggested action or recommended behavior. The matchingof message; interactively therapeutic content; or pushed therapeuticwith EMS type may be pre-defined by at least one of an accredited expertor source; probabilistic; or deep learned. In a preferred embodiment, anaccredited expert or source will require at least two independentsources of peer-reviewed scholarship or data in order to validate thematch.

The at least primary message prescriber 305 may push a message orinteractively therapeutic content personalized to the user based on atleast one stored message matched to the selected EMS. For example,within the EMS store 403, if EMS 1 (lethargic) is selected as defined bythe user or the system, any one of message 1, 2 . . . n may be selectedby the prescriber 305. The pre-defined messages validated by theaccredited expert may all be messages with documented utility inelevating mood and energy (rubric). The mood and energy documented foreach message may be on a scale. For instance, EMS 1 message 1 may below-moderate; EMS 1/message 2 may be moderate; and EMS 1/message n maybe high-severe, etc. Any variant of the scale may be featured withoutdeparting from the scope of the invention. In other embodiments, themessages, while falling under the same rubric and un-scaled, can varyalong design cues. For instance, the prescriber 305 may choose EMS1/message 2, over other available messages, due to the fact that themessage is comprised of traditionally feminine cues (pink-coloredbauhaus typeface) for a female user. Other user profile or demographicinformation may further inform the prescribers 305 choice of messagetype, such as age, education level, voting preference, etc. User profileor demographic information may be user inputted or digitally crawled.

Still in reference to FIG. 11, the prescriber's 305 choice of messagetype is not specific to a user, user profile, or crawled user data. In acertain embodiment, the prescriber 305 may have to choose between anyone of the message types (message 1, message 2 . . . message n) from theselected EMS type. This type of message assignment may be completelyarbitrary. In other embodiments, the message assignment may be notspecific to a user-generated or crawled profile but may be based on userhistory. In other words, a user's tracked level of engagement with aprevious message or message from a previous session may inform messageassignment by the prescriber 305. Tracking engagement of a user with apushed or prescribed therapeutic message may be by camera-captured eyegazing, touch-screen interaction, time span between pushed therapeuticand user follow-up action, choice of follow-up action, etc.

In some embodiments, the full list of message types is not grouped byEMS type or along any design categories, but rather simply listedarbitrarily and mapped or matched to an appropriate EMS type. In thisarbitrarily listed manner, the prescriber 305 may match to more than oneEMS type. Likewise, a user may be defined by more than one EMS type andbe prescribed the same message type.

FIG. 12 illustrates a flow diagram depicting the method of delivering adigital therapeutic in accordance with an aspect of the invention. In apreferred embodiment, the method may comprise the steps of: (1)recognizing at least one EMS selected by the user from a plurality ofEMS, the selected EMS indicating at least one of a feeling, sensation,type of discomfort, mood, mental state, emotional condition, or physicalstatus of the user 508. Once the EMS is defined, the method then callsfor (2) pushing at least a primary-level message personalized to theuser based on at least one stored message coupled to the selected EMS509.

In some embodiments, the system or method may call for pushing at leasta secondary-level message personalized to the user based on athreshold-grade match of the user response to the pushed primary-levelmessage with at least one stored response coupled to a storedprimary-level message, whereby the user and stored response is a measureof at least one of a reaction, compliance, engagement, or interactivitywith the pushed and, or stored primary-level message. Much like theprimary message or primary-level message, the secondary-level messagesmay also contain at least one of a text, image, sound, video, art asset,suggested action or recommended behavior. Again, the efficaciousness ortherapeutic value of the primary or secondary messages are validated byat least one—and typically two—independent sources of clinical researchor peer-reviewed science, as verified by a credentialed EMS expert.

In order to facilitate the at least secondary message or secondary-levelmessage, the primary prescriber 305 may be used: Assigning a secondmessage to the same user in the same session for the first defined EMStype. As is with the assignment of the first message, the assignment ofthe second may arbitrarily choose among EMS-grouped messages or from thefull arbitrary list of messages in the EMS store. Moreover, the primaryprescriber 305 may perform the secondary assignment in a logic-definedmanner, wherein gathered, contextualized, or profiled data informs theassignment. In yet other aspects, second-level assignment may beperformed by at least a secondary message prescriber 307, wherein the atleast secondary message prescriber 307 pushes at least a secondary-levelmessage personalized to the user based on a threshold-grade match of theuser response to the pushed primary-level message with at least onestored response coupled to a stored primary-level message, whereby theuser and stored response is a measure of at least one of a reaction,compliance, engagement, or interactivity with the pushed and, or storedprimary-level message.

For instance, when a user-generated or system-generated EMS is definedas ‘unfulfilled’ for user A, a primary prescriber 305 assigns message 2(uplifting; inspiring message) from EMS 1 (unfulfilled). In oneembodiment, a secondary prescriber 307 prescribes a pro-social behavior,such as a local community service, immediately upon a touch interactionwith the first inspiring message pushed. In other embodiments, a levelof engagement, interaction or compliance may be tracked by the system toinfer severity of the EMS. For instance, if user A does not comply withthe touch-interaction requests from the first inspiring message orpro-social behavior recommendation of the second message, then thesecondary prescriber 307 may push a less physically strenuous pro-socialrecommendation, such as suggesting to call an in-network licensed expertor simply make a cash donation to a charitable organization of the userschoosing via a linked micro-payment method. For the purposes ofinferring severity of EMS, any number of diagnostics that leverage anyone of the on-device tools may be used, such as gyroscopic sensors orcameras. Secondary assignment may also be based on learned history, suchas a past positive reaction (compliance) to a receiving a message from aloved one that a donation was made in user A's name to a charitableorganization. Based on such history, a secondary prescriber 307 mayassign a primary or secondary message recommending to make a donation inthe name of a loved one during an ‘unfulfilled’ EMS experienced by userA.

The processing unit may further be communicatively coupled to at leastone of an interface module, display module, input module, logic module,a context module, timeline module, tracking module, notification module,and a payment/gifting module. In accordance with one aspect, thenotification module may be configured to generate reports at regularintervals (such as daily at 12:00 PM, weekly and monthly), on-demand(when the user requests for a report corresponding to the user), whentriggered by an event, or upon a detected severe EMS. In an embodimentof the present invention, the notification module may also be configuredto send a notification to the user or to a chosen loved one of the user.The notification may be a message, a phone call or any othercommunication means.

In an embodiment of the present invention, a timeline module may pushalready pushed messages in at least one of a static, dynamic, and, orscheduled fashion based on at least one of the user's schedulercriteria. The line of static, dynamic, and, or scheduled messages may becurated by the user, pre-set, or dynamically pushed based on any one ofa user parameter. In some embodiments, the timeline module enables thedisplayed line of static, dynamic, and, or scheduled messages to befurther replicated on at least one of a social media timelines orstories. In other words, the timeline module enables the displayedmessages to be further shared with social media outlets.

In an embodiment of the present invention, a payment or gifting modulemay enable purchasing and gifting donations, physical objects, ordigital assets. The gifting module may further be coupled to adistributive digital ledger, wherein each transaction among any user isrepresented as a unique node in the digital ledger. Each node taggedwith meta data facilitating at least one of a transaction, validationand, or registration for each transaction.

FIG. 13 is a representative screen shot depicting an exemplary userinterface in accordance with an aspect of the invention. As shown, thetop layer 602 depicts a spotlighted EMS and the bottom layer is a scrollmenu of EMS. In this case, the concept of EMS, as earlier defined, alsoincludes behavioral interventions or training regimens, in addition toan emotional and mental state. In some embodiments, an exemplary userexperience may have both top layer 602 and bottom layer 604 within thesame screen, wherein the top layer 602 is a spotlighted rendering of thefocused EMS from the EMS menu depicted in the bottom layer 604. In otherembodiments, the window may only feature the scrolling EMS menu asdepicted in the bottom layer 604, wherein the focused EMS from theplurality of EMS may pop-out, or be emphasized anyhow. In yet otherembodiments, the window may only feature the one EMS at a time, allowingfor the user to go through the entire menu, one window (EMS) at a time.In yet other embodiments, the menu may be featured in a thumbnailformat, allowing the user to choose at least one EMS from a thumbnailmenu, sized to fit in a single window, or alternatively, configured forscrolling.

FIG. 14 is a representative screen shot depicting an exemplary userinterface in accordance with an aspect of the invention. Once the EMS(behavioral intervention or training regimen) is defined, users can readmore about the intervention or training regimen they're going to startand self-administer (have pushed to their device) from a top portion ofthe card (window) 702. On the same card (window), the bottom portion mayhighlight proven benefits, and then provide directions for use, mixingreal guidance with elements of humor 704. The medical-inspiredalliteration and iconography are intended to invoke a sense ofprescriptive health care or wellness.

FIG. 15 is a representative screen shot depicting an exemplary userinterface in accordance with an aspect of the invention. As shown, oncethe EMS (regimen) is defined and a particular course of treatment(message) is started, on the top-right portion of the next cardexplicitly identifies the specific drug benefit 802. While not shown, bytapping the drug abbreviation, users can see the source of supportingscientific research 802. By tapping the hamburger icon, users can chooseto save the individual card, or share the card and its contents withfriends across social media. It is to be understood by a person ofordinary skill in the art that these icons, or any icons, on this card(window), or any card (window), may be positioned elsewhere (oranywhere), without departing from the inventive scope.

The focal point of the card (window) is the actual EMS-defined message(treatment), and in the case of this window, is a suggested action—jumpfor 5 seconds. Jumping for 5 seconds is a suggested action to restorethe oxytocin neurotransmitter, which is documented for buildinghappiness and confidence—the initially chosen EMS or behavioralintervention by the user (FIG. 13). The veracity of the message orsuggested action is supported by the referenced peer-reviewed researchand co-signed credentialed expert 802. As a person, skilled in the artwill recognize from the previous detailed description and from thefigures and claims, modifications and changes can be made to theembodiments of the cards, windows, icons, design elements, EMS types,behavioral intervention types, message types, without departing from thescope of this invention as defined in the following claims.

While not shown in FIG. 15, the messages (cards/windows) may comprise asingle or battery of physical and, or cognitive tasks and based onresponses, further indicate a more nuanced EMS for a more tailoredinitial or subsequent message. Responses may include a level ofcompliance, engagement, interaction, choices, etc. Furthermore, fordeeper and more nuanced EMS definition, assigning an indication score orcolor-coded range to further convey EMS severity may be achievable. As aresult, matching of message type to scored or color-coded EMS mayproduce a more refined match for pushing of even more personalizeddigital content or therapeutics.

FIG. 16 illustrates a flow diagram depicting the method of rating orlabeling a digital therapeutic to digital content in accordance with anaspect of the invention. In a preferred embodiment, the method maycomprise the steps of: (1) uploading digital content 902; (2) selectingat least one condition from a plurality of conditions that the uploadeddigital content is intended to cure, said selected condition indicatingat least one of a feeling, sensation, mood, mental state, physicalstate, emotional condition, physical status 904; and (3) overlaying atherapeutic label to the digital content corresponding to the selectedcondition 906.

The selected condition may be an EMS (emotional mental state indicator)indicating at least one of a feeling, sensation, type of discomfort,mood, mental state, emotional condition, or physical status of the user508. Once the EMS is defined and labeled, the method may then call forpushing at least a subsequent or battery of messages/contentpersonalized to the user based on the initially labeled EMS 509. In someembodiments, the system or method may call for pushing at least asubsequent message or battery of messages personalized to the user basedon a user response or interaction to the uploaded digital content and,or to the pushed primary/initial/level message. User response orinteraction may be based on a threshold-grade match of the user responseto the uploaded digital content and, or to the pushed primary-levelmessage with at least one stored response coupled to a stored uploadedcontent/primary-level message, whereby the user and stored response is ameasure of at least one of a reaction, compliance, engagement, orinteractivity with the uploaded digital content and, or pushedprimary-level message. Much like the uploaded digital content—whethersimply uploaded, created, or curated—the primary message orprimary-level message and the subsequent/battery messages may alsocontain at least one of a text, image, sound, video, art asset,suggested action or recommended behavior. The digital content mayfurther contain an animation, infographic, meme, GIF, chat, post, andaudio. The digital content uploaded by the user is originated from atleast one of a stored, received, visited, curated, and created source.

In continuing reference to FIG. 16, the prescribed label overlaid on theuploaded digital content may be at least one of a drug type,neurotransmitter type, therapeutic type matched to the selected EMStype. In other embodiments, the EMS may encompass not only thecondition, but also the drug type, neurotransmitter type, and, ortherapeutic type (cure). In other embodiments, at least one of the EMStype, condition, cure may be based on a scored or color-coded aspects toindicate severity. Assessing an indication score or color-coded range tofurther convey at least one of an EMS severity, intended effect range,and therapeutic efficacy may be possible. In some embodiments, theefficaciousness or therapeutic value of the uploaded content, primary,and, or secondary messages are validated by at least one—and typicallytwo—independent sources of clinical research or peer-reviewed science,as verified by a credentialed EMS expert.

Now in reference to FIG. 17, a system depicted as a block diagram,wherein the processing system (1008) and modules (1008 a-d) arespecifically interrelated and configured to perform a particularsub-routine in accordance with at least one of a defined logic,probabilistic learning (machine learning/AI), statistical modeling, orrules, in order to achieve labeling of a therapeutic value to anuploaded digital content. In one embodiment, the user may upload thecontent and select the content type and treatment type (FIG. 19).Examples of content type may be video, music, film clip, GIF, photo,PDF, screen shot, social media post, text message template, VR asset, ARasset. The user may choose on or more of the content types to informmore accurate therapeutic labeling of the uploaded content. Uponchoosing the content type, the user may choose one or more treatment orcondition types (EMS) that most correlate with the uploaded content. Insome embodiments, a user may only need to choose the treatment/condition(EMS) type.

In continuing reference to FIG. 17, the content type and treatment typemay be autonomously generated without user input or data. The contentreviewer 1008 a may take multiple bound-boxed crops from at least one ofa 2D or 3D parsed or non-parsed image frame, perform object or eventdetection, and then join crops to form a mask for the original image.The reconstructed mask or loose crops are then stitched together andbased on at least one of an object detected, facial feature, overallcontext, emotional cues, stylistic elements, deconstructed text and, oraudio, at least one condition/EMS from a plurality of conditions/EMS isselected by the condition selector 1008 b, said selected conditionindicating at least one of a feeling, sensation, mood, mental state,physical state, emotional condition, physical status. Once theappropriate condition/EMS is selected, the therapeutic labeler 1008 cwill assign a therapeutic label to the digital content corresponding tothe selected condition by the therapeutic labeler based on aseverity-graded look-up table (represented on a high-level and withoutseverity-grading by the quick reference guide—FIG. 23.

While not shown in FIG. 17, the method or system may comprise an optionto upload a digital content by a user; parse the uploaded digitalcontent into frames for object/event identification. In someembodiments, object/event identification comprises isolating individualframe into cropped defined structures by the content reviewer. In someembodiments, processing the cropped frames through at least one of aconvolutional classifier network or convolutional semantic segmentationnetwork. In other embodiments, object/event identification does notrequire processing using a convolutional classifier network orconvolutional segmentation network. Once identified (i) oridentified/processed (i-p), match at least one (i/i-p) frame against alibrary of stored content indicating at least one selected condition bythe condition selector, said condition being at least one of a feeling,sensation, mood, mental state, physical state, emotional condition,physical status. Finally, overlay a therapeutic label to the uploadeddigital content corresponding to the stored content with selectedcondition above a matched threshold by the therapeutic labeler.

At least one of content review, condition selection, and therapeuticlabeling may be achieved by analyzing at least one of computed pixelvalues derived from at least one of a parameter from a threshold-gradeevent or object by referencing against at least one of a pre-defined,user-defined, and, or learned reference table of recognizedobject/event-computed pixel values. Any number of may employ machinelearning to update any one of a threshold of computed pixel values forobject/event detection and, or update any one of a reference analysis ofcomputed pixel values for condition selection/therapeutic labeling.Examples of machine learning may be at least one of a convolution neuralnetwork, associated model, training data set, feed-forward neuralnetwork, and, or back-propagated neural network.

Still in reference to FIG. 17, the system may further comprise a pushedname or list of names of in-network or out-of-network members with atleast one of a self-identified or system-generated EMS receptive to thelabeled content, with an option to send the labeled content to at leastone of the pushed name or list of names. In other embodiments, a blindpush of the labeled content to at least one of the pushed name or listof names may be possible. Furthermore, the in-network or out-of-networkmember receiving the labeled content may be tracked by at least one ofan off-board camera, sensor, compliance or performance to at least oneof a cognitive or physical task request.

In other embodiments, the primary prescriber 305 may be used to do atleast one of a content review, condition/EMS selection, and overlay of atherapeutic label to a digital content. Assigning a second message tothe same user in the same session for the first defined EMS type. Theprimary prescriber 305 may perform at least one of a content review,condition/EMS selection, and therapeutic label overlay in alogic-defined or rule-based manner, wherein gathered, contextualized, orprofiled data may further inform at least one of the content review,condition/EMS selection, and overlay.

For instance, when a system-generated EMS is selected as ‘Love’ for userA, a primary prescriber 305 or therapeutic labeler 1008 c assigns atherapeutic label (Serotonin: uplifting and inspiring message tostabilize mood and provide stability for happiness to flourish). In apreferred embodiment, the therapeutic label may also be tapped forproviding additional information, such as drug/neurotransmitterinformation, benefits, and citations (FIG. 20). In one embodiment, asecondary prescriber 307 may push a subsequent message or content, suchas a pro-social behavior, such as a local community service, immediatelyupon a touch interaction with the first inspiring message pushed. Inother embodiments, a level of engagement, interaction or compliance maybe tracked by the system to infer severity of the EMS. For instance, ifuser A does not comply with the touch-interaction requests from thefirst inspiring message or pro-social behavior recommendation of thesecond message, then the secondary prescriber 307 may push a lessphysically strenuous pro-social recommendation, such as suggesting tocall an in-network licensed expert or simply make a cash donation to acharitable organization of the users choosing via a linked micro-paymentmethod. For the purposes of inferring severity of EMS, any number ofdiagnostics that leverage any one of the on-device tools may be used,such as gyroscopic sensors or cameras. Severity may also be inferredfrom contextual data gathered from off-board devices, Iot objects,crawled social media data, etc.

In one embodiment, therapeutic labeling of uploaded digital content maybe based on learned user history, such as previous labeling history and,or engagement/reaction (compliance/non-compliance) to receiving amessage/content. Based on such history of labeling and, or engagement, aprescriber 307 or therapeutic labeler 1008 c may assign a therapeuticlabel for uploaded content by user A that is consistent or departed fromthe previous labeling.

FIG. 18 is a representative interaction flow of the therapeutic labelersystem in accordance with an aspect of the invention. In a preferredembodiment of the invention, the inputs 1101 recognizes a command andprocesses input from anyone of a user's device or user, wherein theinput is any one of a digital content uploaded from a user. The digitalcontent uploaded by the user is originated from at least one of astored, received, visited, curated, and created source. Furthermore, thecontent may be at least one of saved, processed, edited, and uploaded inedited form; or uploaded in original/received form; and forwarded to thedownstream system that provides the recognized command for enablingtherapeutic labeling of the digital content.

In an embodiment of the invention, the inputs 1101 may be motioncharacteristics corresponding to at least one of, physical activity,physiological and sleep related characteristics of a user quantifiedfrom a body worn or user device. Additionally, inputs 1101 may accountfor environmental conditions, such as wind velocity, temperature,humidity, aridness, light, darkness, noise pollution, exposure to UV,airborne pollution and radioactivity quantified from a body-worn/userdevice and, or remote stations. Further yet, data generated from aperiodic survey pushed to a body worn/user device may be used togenerate a behavioral profile of the user, which may serve as an input1101 or inform an input 1101. The system may flag a thresholddiscrepancy between a composite behavioral profile and a referencebehavioral profile to detect or select an appropriate condition/EMS, inaddition to the parsed digital content by the content reviewer 1102,condition selector 1102, therapeutic labeler 1102, whereby theappropriate condition/EMS is determined by machine learning algorithmsto trigger a number of downstream provisionings 1104.

Further yet, in another embodiment, the system may further compriseintegration with any one of a third-party application via an ApplicationProgram Interface (API) 1104. This allows for 3rd party databaseintegration, such as Electronic Medical Records (EMR), healthmonitoring, proxy health provisioning, remote server and, or a cloudbased server for other downstream analytics and provisioning.Additionally, the completed automated responses may be saved onto aremote cloud based server for easy access for data acquisition andarchival analytics for future use.

In another embodiment of the invention, the system may allow for easysaving, searching, printing, and sharing of completed automated responseinformation with authorized participants. Additionally, the system mayallow for non-API applications, for example, building reports andupdates, create dashboard alerts as well as sign in/verifications 1104.Alternatively, sharing may be possible with less discrimination based onselect privacy filters. Moreover, the system may be integrated withcertain workflow automation tools, prompting the system to perform atask command, provided a trigger is activated based on the thresholddiscrepancy. In an embodiment of the invention, at least one conditionalevent triggers at least one action controlled by a “if this, then that”1104 script manager. Further yet, the “if this, then that” 1104 scriptmanager is embedded with an “and, or” trigger or action operators,allowing increased triggers or actions in a command set.

In another instance, the script manager may be embedded with a “if,this, then that” as well as a “and, or” trigger or action operator forincreased triggers either downstream or upstream of a command set. Whilenot shown in FIG. 18, “IF” a user uploads content with an EMS rating ofsad, “THEN”, the user will be sent prescriptive content to counter thesadness, such as Serotonin boosting content (see FIG. 22 as arepresentative screenshot) “AND” the users closest friend will receivean email/text reminder to get in touch with the user. All of thecommands are automatically triggered once an “IF” conditional event isreached.

In yet another embodiment of the invention, “OR” operators may be usedinstead of the “AND” operator. Further, any number of “AND” and, or “OR”operator may be used in a command function. Such an automation layer mayadd further efficiencies. An ecosystem of apps may provide for aAPI-mediated link to the system for enhanced co-interactivity amongusers network, diagnostics, and other measurables.

The processer system 1102 may further be communicatively coupled to atleast one of a provisioning module 1103, interface module, displaymodule, input module, logic module, a context module, timeline module,tracking module, notification module, payment/gifting module, andmarketplace module in order to effectuate any number of remoteprovisioning. In accordance with one aspect, the notification module maybe configured to generate reports at regular intervals (such as daily at12:00 PM, weekly and monthly), on-demand (when the user requests for areport corresponding to the user), when triggered by an event, or upon adetected severe EMS. In an embodiment of the present invention, thenotification module may also be configured to send a notification to theuser or to a chosen loved one of the user. The notification may be amessage, a phone call or any other communication means.

In an embodiment of the present invention, a timeline module may pushalready pushed messages in at least one of a static, dynamic, and, orscheduled fashion based on at least one of the user's schedulercriteria. The line of static, dynamic, and, or scheduled messages may becurated by the user, pre-set, or dynamically pushed based on any one ofa user parameter. In some embodiments, the timeline module enables thedisplayed line of static, dynamic, and, or scheduled messages to befurther replicated on at least one of a social media timelines orstories. In other words, the timeline module enables the displayedmessages to be further shared with social media outlets.

In an embodiment of the present invention, a payment or gifting modulemay enable purchasing and gifting donations, physical objects, ordigital assets. In an embodiment of the present invention, a marketplacemodule may enable purchasing digital assets. The gifting and marketplacemodule may further be coupled to a distributive digital ledger, whereineach transaction among any user is represented as a unique node in thedigital ledger. Each node tagged with meta data facilitating at leastone of a transaction, validation and, or registration for eachtransaction.

FIG. 24 illustrates a flow diagram depicting the method of rating orlabeling a digital therapeutic to digital content based on meta-datatags in accordance with an aspect of the invention. In a preferredembodiment, the method may comprise the steps of: (1) uploading digitalcontent 2402; (2) selecting at least one intended psycho-emotionaleffect that the uploaded digital content causes based on at least onemeta-data tag scraped from the content 2404; and (3) overlaying atherapeutic label to the digital content corresponding to the intendedpsycho-emotional effect, wherein the label serves as an viewerdiscretion warning or advisory to a viewer prior to viewing 2406.

While not shown, the digital content may be an advisory labeled withintended psycho-emotional effect on a viewer from any site or platformwithout an active upload from the viewer. The viewer may simply instructa desire to stream the content from any site/platform (hitting play) andprior to playing, the therapeutic labeling system will scrape theback-end data for meta-tags to assess an intended psycho-emotionaleffect on the viewer and prominently label the content as an advisoryaccordingly—prior to viewing. The label may be the exact intendedpsycho-emotional effect assessed or a short-hand corresponding to theintended psycho-emotional effect or EMS (emotional/mental stateindicator). The EMS indication is at least one of a feeling, sensation,type of discomfort, mood, mental state, emotional condition, or physicalstatus of the user. In addition to the EMS, another short-handindication of the assessed psycho-emotional effect may be aneurotransmitter (NT) label such as Dopamine (DA), Acetylcholine (Ach),Serotonin (Se/5-HT), etc.

The short-hand therapeutic label overlaid on the uploaded/streameddigital content may be at least one of a drug type, NT type, EMS type,and/or therapeutic type matched to the selected EMS type. FIG. 20illustrates a representative screen-shot depicting a content prior tostream/play with a standard advisory therapeutic label. As shown, a NTlabel is displayed, more particularly, a Dopamine (DOPA-purple square)and GABA (turquoise ellipse) label displayed as a color-coded symbol,indicating to a viewer that the content, if viewed, has the potentialfor a strong audio stimulation and an overall sense of calmness. Inother embodiments, the label may be clicked on for a long-form versionwith expandable/collapsible information regarding specific time-stampedscenes or elements in the content that are expected to trigger DOPA/GABA(optionally, any other NT, or combination of NT's); physiological and/ormental health benefits for elevating DOPA/GABA (optionally, any otherNT, or combination of NT's); suggestions for alternate content and/ortitles for triggering DOPA/GABA (optionally, any other NT, orcombination of NT's); or suggestions for alternate activities/events fortriggering DOPA/GABA (optionally, any other NT, or combination of NT's).

FIG. 21 illustrates an exemplary screen-shot of a routinely visited webpage featuring a scraped digital nutritional label or profile inaccordance with an aspect of the invention. The digital nutritionallabel or profile consists of a breakdown of the variousneurotransmitters that may potentially be elicited as a result ofviewing or browsing the web page content. As opposed to the short-handlabel featured in FIG. 20, the digital nutritional labeling provides aquantitative profile of each neurotransmitter potentiallyimplicated—irrespective of how minor the dosage is. For instance, Jenmay really be seeking OXY-rich content due to her circumstances of beingsocially isolated on the campus of UC Berkley during the Thanksgivingholidays, and as a result, avoid the BMW web page excerpted in FIG. 21due to its exceptionally low OXY dosage.

In other embodiments, while not shown in FIG. 20 or 21, the label mayhave the option to collapse or expand on the additional information,thereby allowing a user to convert a label to a full-form label. Inother embodiments, each of the NT short-hands illustrated in the quickreference guide form in FIGS. 23 and 24 may be coded in any number ofways. For instance, Serotonin (depicted as a color-coded circlecorresponding to a specific sub-set of Serotonin benefits/effects) couldbe any color-coded shape, as per a pre-defined protocol. Furthermore,the short-hands in FIG. 24 could be communicated with a scored orcolor-coded aspect to indicate severity. For instance, content with alabel featuring a dark blue-colored circle, versus a light blue-coloredcircle, should advise a viewer of the contents high-energy themes Insome embodiments, the therapeutic value of the uploaded content isfurther validated by at least one—and typically two—independent sourcesof clinical research or peer-reviewed science and a credentialed mentalhealth science expert.

Now in reference to FIG. 25, a content labeling system depicted as ablock diagram, wherein the processing system (2508) and modules (25008a-d) are specifically interrelated and configured to perform aparticular sub-routine in accordance with at least one of a definedlogic, probabilistic learning (machine learning/AI), statisticalmodeling, or rules, in order to achieve labeling of a therapeutic valueto an uploaded/stream initiated digital content. In one embodiment, theuser may upload/initiate stream of the content and select the contenttype and treatment type desired (FIG. 19). Examples of content type maybe video, music, film clip, GIF, photo, PDF, screen shot, social mediapost, text message template, VR asset, AR asset. The user may choose onor more of the content types to inform more accurate therapeuticlabeling of the uploaded content. Upon choosing the content type, theuser may choose one or more treatment or condition types (EMS) desiredfrom the content. In some embodiments, a user may only need to choosethe treatment/condition (EMS) type. In some embodiments, the contenttype and/or treatment/EMS type may be bypassed.

In continuing reference to FIG. 25, the content type and treatment typemay be autonomously generated without user input or data. Once theappropriate condition/EMS-NT (intended psycho-emotional effect on aviewer) is selected by the condition selector/EMS-NT scraper 2508 bbased on a meta-tag scrape, the therapeutic labeler 2508 c will assign atherapeutic label to the digital content corresponding to the selectedcondition based on a severity-graded look-up table (represented on ahigh-level and without severity-grading by the quick referenceguide—FIG. 23).

The condition selector/EMS-NT scraper 2508 b may perform the meta-tagscraping of video content in similar fashion to a crawler or meta-dataparser would for a web page: Query for meta-tags with certain prefixessuch as title, description, keyword, thumbnail images, emotional-chargedkeywords, etc. The detection of these prefixes could then inferparticular EMS/NT's or psycho-emotional effects intended from thecontent. By scraping, rather than parsing, certain efficiencies may beachieved—most notably, speed and lowered bandwidth demands.

In another embodiment, labeling may take into account the aggregated orspot-checked digital nutrition profile of the viewer in an effort tolabel the digital content personalized to the viewer. In other words,the same digital content may be labeled differently to two differentviewers due to their disparate profiles. To exemplify, Zeeshan—notexactly physically active—may be pushed a DA (Dopamine—sky bluesquare—thrill seeking) label for a video related to the Tour de France,whereas Yitzhak, who recreationally drives a sports-tuned motorcycle,may be pushed a DA (turquoise square—sense of well-being/calm) for thesame exact clip.

In another embodiment, rather than just a short-hand or long-form NTlabel, a digital nutrition label may be overlaid. The digital nutritionlabel may be a composition of a variety/plurality of NT's implicated oreffected by viewing the content. For instance, while content may have apredominate theme of a single NT or psycho-emotional effect triggered,there may be a variety of disjointed or interrelated sub-themes/sub-NT'sthat are expressed and deserve labeling. The digital nutrition labelingmay be in the form of a standard nutrition label found on the box ofshelved-food items or in the form of a color-palette/wheel/map, etc.

In another embodiment, ad retrieval from the DN Ad store for triggereddelivery by the DN Ad player is not score or range specific, but rather,just based on broad EMS/PEE grouping. For instance, returning to thescenario of Zeeshan, his ACh+++ labeled content was not off-set by thefollowing short-clip of Luca Doncic crossing over an opponent—rated bythe labeler several weeks ago with a ‘Dopamine’ or ‘DA’. As a result,the tracker has tracked Zeeshan over the past two views with apsyho-emotional effect evoked (EMS/PEE) of a ACh+. With this slightlyrevised down ‘ACh+’ EMS/PEE rating, the DN tracker or DN Playerretrieves an Ethan Allen spot for a patio set rated ACh—ACh++. In otherembodiments, Zeeshan's tracked ACh+ rating will retrieve a “Cool Grey”Jumpman retro 4 ad based on the fact that one of the two viewsprominently featured Luka Doncic wearing grey basketball sneakers. Thistype of object detection and matching similar objects from an ad mayobviate the need of score matching.

The DN labeling for targeted ad delivery may not require label trackingof viewed content, but rather just assign a digital nutrition (DN) labelto the uploaded digital content, wherein said label is an indication ofthe intended psycho-emotional effect of said content; and triggerdelivery of a targeted advertisement from a store, wherein the targetedadvertisement is labeled with a digital diet score range covering for ascore corresponding to the last viewed labeled content prior to anadvertisement delivery trigger point. Tracking is obviated by simplyrelying on a ‘last content viewed’ approach.

To further clarify, in continuing reference to Yitzhak, though his lastviewed content was action/thrill-seeking/DA++ rated (man swimming totrain for an upcoming triathalon), a Serotonin/5-HT3 rated ad featuringan elderly man fishing against a soothing backdrop for an Acid-Refluxover-the-counter generic may be pushed by the Ad Player, rather than theRed Bull spot, for its countering effect or value. Though countering theDA++ rating of the viewed content, the Ad Player decision to choose a5-HT3 rated ad with a common water feature truly reflects the level ofnuance that may be incorporated in the delivery of targeted advertising.In addition to labeling, retrieval and delivery of targeted advertisingmay further take advantage of profile data or contextual data(geo-location, date/time, temperature, sensor-captured data, etc.) tofurther personalize for maximal branding impact.

The claimed invention leverages existing clinical research and provenscience (already published in peer-reviewed journals) and repackagesinsights as content modules or behavioral interventions that aresimpler, more seductive, and profoundly more fun than traditionalanalogue therapies or digital treatment regimen. Described more simply,the system and platform curates existing digital content, and createsentirely new content programs, informed by and centered aroundtechniques proven to boost mood, alleviate anxiety, reduce stress, andimprove psychological health or mental fitness by directing users tofollow procedures proven to increase the production of beneficialmolecules and neurotransmitters like Dopamine, Oxytocin, Acetylcholine,Serotonin, and GABA to deliver positive mood and mind-altering effects.This is, in essence, a purely digital, transorbital drug deliverysystem. No pills. No powders. Purely digital experiences to positivelyimpact mood, mind and personal sense of well-being.

Embodiments are described at least in part herein with reference toflowchart illustrations and/or block diagrams of methods, systems, andcomputer program products and data structures according to embodimentsof the disclosure. It will be understood that each block of theillustrations, and combinations of blocks, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general-purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner such that the instructions stored in the computer-readable memoryproduce an article of manufacture including instruction means whichimplement the function/act specified in the block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus, to produce a computer implemented process such that, theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions/acts specified inthe block or blocks. In general, the word “module” as used herein,refers to logic embodied in hardware or firmware, or to a collection ofsoftware instructions, written in a programming language, such as, Java,C, etc. One or more software instructions in the unit may be embedded infirmware. The modules described herein may be implemented as eithersoftware and/or hardware modules and may be stored in any type ofnon-transitory computer-readable medium or other non-transitory storageelements. Some non-limiting examples of non-transitory computer-readablemedia include CDs, DVDs, BLU-RAY, flash memory, mobile device, remotedevice, and hard disk drives.

The invention claimed is:
 1. A method for labeling digital content basedon intended psycho-emotional effect on a viewer in order to provide anadvisory prior to viewing, said method comprising the steps of:uploading or stream-initiating a digital content by a viewer; selectingat least one intended psycho-emotional effect from the content based onscraped meta-data tags of the content; and overlaying a digitalnutrition label to the digital content corresponding to the scrapedpsycho-emotional effect from the content, wherein the overlaid labeldisplays at least one of an effect or triggered neurotransmitterassociated with the effect in order to provide an advisory to the viewerprior to viewing the labeled digital content.
 2. The method of claim 1,wherein the overlaid label is supported by a parsing or computer visionanalysis of the content for selecting the intended psycho-emotionaleffect of the content.
 3. The method of claim 1, wherein the overlaidlabel is supported by at least one independent source of peer-reviewedresearch.
 4. The method of claim 1, wherein the overlaid label isexpressed as a neurotransmitter effected in an abbreviatedcolor/symbol-coded display.
 5. The method of claim 3, wherein theabbreviated color/symbol-coded display is based on a pre-definedsignaling or communication protocol.
 6. The method of claim 4, whereinthe signaling or communication protocol comprises a spectrum of effectexpressed in a variation of colors of the same symbol corresponding tovarying levels of the same neurotransmitter effected.
 7. The method ofclaim 4, wherein the signaling or communication protocol comprises aspectrum of effect expressed in a score variation associated with theneurotransmitter.
 8. The method of claim 1, wherein the overlaid labelfurther comprises an expandable/collapsible long-form label for at leastone of a digital content type, therapeutic type, drug type, andbenefits.
 9. The method of claim 1, further comprising a pushed name orlist of names of in-network or out-of-network members with at least oneof a self-identified or system-generated psycho-emotional effectreceptive to the labeled content; and an option to send the labeledcontent to at least one of the pushed name or list of names.
 10. Themethod of claim 1, wherein the labeled content informs a push ofadvertisement delivery corresponding to the labeled content in terms ofintended pyscho-emotional effect.
 11. The method of claim 1, furthercomprising a marketplace for exchange of similarly-labeled contentbetween market-subscribed viewers.
 12. The method of claim 1, whereinthe digital content labeled is based on a user context fed from at leastone of a geo-location, social media engagement, and sensor-trackedmotion or activity.
 13. The method of claim 1, wherein the advisorylabel is expressed in the form of a digital nutrition label listing inproportionate form for each neurotransmitter effected from the digitalcontent.
 14. A system for labeling an advisory to digital content, saidsystem comprising: a condition selector; a therapeutic labeler; anon-transitory storage element coupled to the processor; encodedinstructions stored in the non-transitory storage element, wherein theencoded instructions when implemented by the processor, configure thesystem to: upload or stream-initiate a digital content by a viewer;select least one intended psycho-emotional effect from the content basedon scraped meta-data tags of the content by the condition selector; andoverlay a digital nutrition label to the digital content correspondingto the scraped psycho-emotional effect from the content by thetherapeutic labeler, wherein the overlaid label displays at least one ofan effect or triggered neurotransmitter associated with the effect inorder to provide an advisory to the viewer prior to viewing the labeleddigital content.
 15. The system of claim 13, wherein the conditionselector determines an emotional cue from the uploaded orstream-initiated content based on a computer vision parsing of thecontent.
 16. The system of claim 13, wherein the overlaid label isexpressed as a neurotransmitter effected in an abbreviatedcolor/symbol-coded display.
 17. The system of claim 15, wherein theabbreviated color/symbol-coded display is based on a pre-definedsignaling or communication protocol.
 18. The system of claim 16, whereinthe signaling or communication protocol comprises a spectrum of effectexpressed in a variation of colors of the same symbol corresponding tovarying levels of the same neurotransmitter effected.
 19. The system ofclaim 16, wherein the signaling or communication protocol comprises aspectrum of effect expressed in a score variation associated with theneurotransmitter.
 20. The system of claim 13, wherein the overlaid labelfurther comprises an expandable/collapsible long-form label for at leastone of a digital content type, therapeutic type, drug type, andbenefits.
 21. The system of claim 13, further comprising a pushed nameor list of names of in-network or out-of-network members with at leastone of a self-identified or system-generated psycho-emotional effectreceptive to the labeled content; and an option to send the labeledcontent to at least one of the pushed name or list of names.
 22. Thesystem of claim 13, wherein the labeled content informs a push ofadvertisement delivery corresponding to the labeled content in terms ofintended pyscho-emotional effect.
 23. The system of claim 13, furthercomprising a marketplace for exchange of similarly-labeled contentbetween market-subscribed viewers.
 24. The system of claim 13, whereinthe digital content labeled is based on a user context fed from at leastone of a geo-location, social media engagement, and sensor-trackedmotion or activity.
 25. The system of claim 13, wherein the advisorylabel is expressed in the form of a digital nutrition label listing inproportionate form for each neurotransmitter effected from the digitalcontent.
 26. A system for labeling an advisory to digital content, saidsystem comprising: a condition selector; a therapeutic labeler; anon-transitory storage element coupled to the processor; encodedinstructions stored in the non-transitory storage element, wherein theencoded instructions when implemented by the processor, configure thesystem to: upload or stream-initiate a digital content by a viewer;select least one intended psycho-emotional effect from the content basedon scraped meta-data tags of the content by the condition selector; andoverlay an advisory label to the digital content corresponding to thescraped psycho-emotional effect from the content by the therapeuticlabeler.